Abstract

Cancer genomics research aim at identifying cancer-related genes and their contribution to cancer initiation and\ndevelopment. With the rapid development of high-throughput sequencing computational techniques, huge volume of cancer\ngenomics data have been generated. Understanding this data poses great challenges to computational biologists. One of such\nkey challenges is to distinguish driver mutations from passenger mutations. Distinguishing driver mutations which contribute\nto cancer development, from passenger mutationsthat have accumulated in somatic cells but without functional consequences\nis main aim of computational cancer genomics.In this article, we aim to review the recent development of computational\nmodels and algorithms for discovering driver genes or modules in cancer.